Demographics for all included participants.
| Demographics | ||||
| Summary | ||||
| N | Age (years) | Education (years) | Sex (M/F/O) | EHI |
|---|---|---|---|---|
| 621 | 29.27 (6) | 14.31 (2.4) | 336/276/9 | 30.33 (76.91) |
| Race | n |
|---|---|
| White | 452 |
| Black or African American | 57 |
| Asian | 54 |
| Multiple | 51 |
| American Indian or Alaska Native | 5 |
| Native Hawaiian or Other Pacific Islander | 1 |
| Other | 1 |
| Hispanic ethnicity | n |
|---|---|
| No | 554 |
| Yes | 67 |
Demographics for included participants, by handedness group (EHI bins).
| Handedness | N | Age (years) | Education (years) | Sex (M/F/O) | EHI |
|---|---|---|---|---|---|
| Left | 171 | 29.14 (6.27) | 14.25 (2.27) | 90/79/2 | -80.19 (19.96) |
| Mixed | 78 | 29.08 (6.08) | 14.59 (2.32) | 49/27/2 | -5.61 (26.7) |
| Right | 372 | 29.37 (5.88) | 14.28 (2.47) | 197/170/5 | 88.68 (15.52) |
| Left: (EHI <= -40) | Mixed: (-40 < EHI < 40) | Right: (EHI >= 40) | |||||
Within each handedness group, do we see the typical field x level
interaction? That is, do participants show a relative bias for global
shapes in the left visual field (LVF)?
Error bars show 95% CI.
Reaction time is modeled as a linear effect of field, level, and
handedness, using data from every target-present trial with a “go”
response:
lmer( rt ~ field*level*handedness + (1 | subject) )
| Field by level by handedness interaction (RT) | |||||||
| ANOVA: compare models with vs. without interaction term | |||||||
| npar | AIC | BIC | logLik | deviance | Chisq | Df | p.value |
|---|---|---|---|---|---|---|---|
| 9 | 892,932.348 | 893,014.217 | −446,457.174 | 892,914.348 | - | - | - |
| 10 | 892,932.367 | 893,023.332 | −446,456.183 | 892,912.367 | 1.982 | 1 | .16 |
| Field by level interaction (RT) | |||||
| Omnibus F-test | |||||
| term | df | sumsq | meansq | statistic | p.value |
|---|---|---|---|---|---|
| field | 1 | 585,689.776 | 585,689.776 | 8.377 | .004 |
| level | 1 | 7,703,209.967 | 7,703,209.967 | 110.176 | <.0001 |
| handedness | 1 | 1,705,109.75 | 1,705,109.75 | 24.388 | <.0001 |
| field:level | 1 | 2,486,971.257 | 2,486,971.257 | 35.57 | <.0001 |
| field:handedness | 1 | 667,410.774 | 667,410.774 | 9.546 | .002 |
| level:handedness | 1 | 68,165.27 | 68,165.27 | 0.975 | .32 |
| field:level:handedness | 1 | 45,604.248 | 45,604.248 | 0.652 | .42 |
| Residuals | 65,932 | 4,609,771,276.462 | 69,917.055 | - | - |
| Field by level by handedness interaction (RT) | |||||||||
| Compare effect estimate to zero with emmeans() | |||||||||
| field_consec | level_consec | handedness_consec | estimate1 | SE | df2 | asymp.LCL3 | asymp.UCL3 | z.ratio | p.value4 |
|---|---|---|---|---|---|---|---|---|---|
| LVF - RVF | Local - Global | Right - Left | 9.786 | 6.952 | Inf | −3.84 | 23.411 | 1.408 | .16 |
| 1 A positive number means LVF global bias is stronger in right handers (as predicted by AAH) | |||||||||
| 2 Z-approximation | |||||||||
| 3 Confidence level: 95% | |||||||||
| 4 Two-sided | |||||||||
| LVF Global bias by handedness bin (RT) | |||||||||
| field_consec | level_consec | handedness | estimate1 | SE | df2 | asymp.LCL3 | asymp.UCL3 | z.ratio | p.value4 |
|---|---|---|---|---|---|---|---|---|---|
| LVF - RVF | Local - Global | Left | 17.497 | 5.747 | Inf | 6.234 | 28.76 | 3.045 | .002 |
| LVF - RVF | Local - Global | Mixed | 20.542 | 8.493 | Inf | 3.897 | 37.188 | 2.419 | .02 |
| LVF - RVF | Local - Global | Right | 27.282 | 3.898 | Inf | 19.643 | 34.922 | 6.999 | <.0001 |
| 1 A positive number means global bias (faster RT for global) | |||||||||
| 2 Z-approximation | |||||||||
| 3 Confidence level: 95% | |||||||||
| 4 Two-sided, uncorrected | |||||||||
In progress.
Model RT as a linear effect of field, level, and EHI (continuous):
rt_model_ehi <- lmer( rt ~ field*level*ehi + (1 | subject) )
## Use anova() on competing models to test 3-way interaction.
interaction_stats <-
function(model_with_interaction,
model_with_no_interaction) {
return(anova(model_with_interaction, model_with_no_interaction))
}
rt_model_no_interaction <- update(rt_model_ehi, . ~ . - field:level:ehi)
interaction_anova <- interaction_stats(rt_model_ehi, rt_model_no_interaction)
interaction_anova |>
as_tibble() |>
rename(p.value = `Pr(>Chisq)`) |>
format_p.value() |>
pretty_table() |>
tab_header(title = "Field by level by ehi interaction (RT)",
subtitle = "ANOVA: compare models with vs. without interaction term")
| Field by level by ehi interaction (RT) | |||||||
| ANOVA: compare models with vs. without interaction term | |||||||
| npar | AIC | BIC | logLik | deviance | Chisq | Df | p.value |
|---|---|---|---|---|---|---|---|
| 9 | 1,021,589.879 | 1,021,672.961 | −510,785.94 | 1,021,571.879 | - | - | - |
| 10 | 1,021,589.309 | 1,021,681.622 | −510,784.655 | 1,021,569.309 | 2.57 | 1 | .11 |
| Estimated global bias by field, for EHI of -100 | |||||
| contrast | estimate1 | SE | df | z.ratio | p.value |
|---|---|---|---|---|---|
| (LVF Local -100) - (LVF Global -100) | 31.6 | 4.199 | Inf | 7.525 | <.0001 |
| (RVF Local -100) - (RVF Global -100) | 16.054 | 4.199 | Inf | 3.823 | .0008 |
| 1 Estimated global bias (ms) | |||||
| Estimated LVF Global Bias for EHI of -100 |
| LVF_global_bias |
|---|
| 15.546 |
| Estimated global bias by field, for EHI of +100 | |||||
| contrast | estimate1 | SE | df | z.ratio | p.value |
|---|---|---|---|---|---|
| LVF Local 100 - LVF Global 100 | 35.347 | 2.874 | Inf | 12.297 | <.0001 |
| RVF Local 100 - RVF Global 100 | 7.226 | 2.885 | Inf | 2.505 | .06 |
| 1 Estimated global bias (ms) | |||||
| Estimated LVF Global Bias for EHI of +100 |
| LVF_global_bias |
|---|
| 28.121 |
\[
28.121 - 15.546 = 12.575ms \\
12.575/200 = 0.063ms / EHI unit
\] Each unit change in EHI (-100:100) corresponds to a
0.064ms difference in LVF global bias. This is the
slope estimate given by the summary function:
summary(rt_model_ehi)
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ field:level:ehi + field:level + field:ehi + level:ehi +
## field + level + ehi + (1 | subject)
## Data: aah_for_rt_ehi_model
##
## REML criterion at convergence: 1021574.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.840176 -0.597977 -0.165488 0.371242 7.183305
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 27038.4 164.434
## Residual 42847.0 206.995
## Number of obs: 75454, groups: subject, 621
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 693.4635462 7.2813841 95.23788
## fieldRVF -2.6540189 2.3114254 -1.14822
## levelGlobal -33.4735020 2.2930125 -14.59805
## ehi 0.1482702 0.0881226 1.68254
## fieldRVF:levelGlobal 21.8333224 3.2412040 6.73618
## fieldRVF:ehi -0.1064488 0.0279636 -3.80669
## levelGlobal:ehi -0.0187300 0.0277301 -0.67544
## fieldRVF:levelGlobal:ehi 0.0628729 0.0392231 1.60296
##
## Correlation of Fixed Effects:
## (Intr) fldRVF lvlGlb ehi flRVF:G flRVF: lvlGl:
## fieldRVF -0.160
## levelGlobal -0.161 0.507
## ehi -0.367 0.059 0.060
## fldRVF:lvlG 0.114 -0.713 -0.707 -0.042
## fieldRVF:eh 0.059 -0.368 -0.187 -0.159 0.263
## levelGlbl:h 0.060 -0.187 -0.368 -0.161 0.260 0.507
## fldRVF:lvG: -0.042 0.263 0.260 0.114 -0.367 -0.713 -0.706
Test for a simple correlation between each subject’s EHI and LVF
global bias.
| Subject-level correlation: linear model | ||||
| term | estimate | std.error | statistic | p.value1 |
|---|---|---|---|---|
| (Intercept) | 18.454 | 3.104 | 5.944 | <.0001 |
| ehi | 0.031 | 0.038 | 0.824 | .41 |
| 1 Two-sided | ||||
| Subject-level correlation: Spearman's rho | ||||
| rho | statistic | p.value1 | method | alternative |
|---|---|---|---|---|
| 0.039 | 38,350,957.034 | .16 | Spearman's rank correlation rho | greater |
| 1 One-sided | ||||
In progress. Model accuracy as a binomial
effect of field, level, and EHI (continuous):
acc_ehi_model <- glmer( rt ~ field*level*ehi + (1 | subject), family = "binomial" )
Test for a simple correlation between each subject’s EHI and LVF
global bias.
| Subject-level correlation: linear model | ||||
| term | estimate | std.error | statistic | p.value1 |
|---|---|---|---|---|
| (Intercept) | 2.484 | 0.389 | 6.38 | <.0001 |
| ehi | 0.001 | 0.005 | 0.298 | .77 |
| 1 Two-sided | ||||
| Subject-level correlation: Spearman's rho | ||||
| rho | statistic | p.value1 | method | alternative |
|---|---|---|---|---|
| −0.004 | 40,060,666.437 | .54 | Spearman's rank correlation rho | greater |
| 1 One-sided | ||||